• Journal of the Korean Institute of Navigation
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• v.16 no.1
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• pp.94-97
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• 1992

In this paper, the individual number of the future has depended not only upon the present individual number but upon the present individual age, considering the stochastic process model of individual number when the life span of each individual number and the individual age as a set, this becomes a Markovian. Therefore, in this paper the individual is treated as invariable, without depending upon the whole record of each individual since its birth. As a result, suppose {N(t), t>0} be a counting process and also suppose $Z_n$ denote the life span between the (n-1)st and the nth event of this process, (n{$geq}1$) : that is, when the first individual is established at n=1(time, 0), the Z$Z_n$ at time nth individual breaks, down. Random walk $Z_n$ is $Z_n=X_1+X_2+{\cdots}{\cdots}+X_A, Z_0=0$ So, fixed time t, the stochastic model is made up as follows ; A) Recurrence (Regeneration)number between(0.t) $N_t=max{n ; Z_n{\leq}t}$ B) Forwardrecurrence time(Excess life) $T^-I_t=Z_{Nt+1}-t$ C) Backward recurrence time(Current life) $T^-_t=t-Z_{Nt}$

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.32 no.3
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• pp.235-240
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• 1996

Forecasts of monthly landings of walleye pollock, Theragra chalcogramma, in Korea were carried out by the seasonal Autoregressive Integrated Moving Average(ARlMA) model. The Box - Cox transformation on the walleye pollock catch data handles nonstationary variance. The equation of Box - Cox transformation was Y'=($Y^0.31$_ 1)/0.31. The model identification was determined by minimum AIC(Akaike Information Criteria). And the seasonal ARlMA model is presented (1- O.583B)(1- $B^1$)(l- $B^12$)$Z_t$ =(l- O.912B)(1- O.732$B^12$)et where: $Z_t$=value at month t ; $B^p$ is a backward shift operator, that is, $B^p$$Z_t$=$Z_t$-P; and et= error term at month t, which is to forecast 24 months ahead the walleye pollock landings in Korea. Monthly forecasts of the walleye pollock landings for 1993~ 1994, which were compared with the actual landings, had an absolute percentage error(APE) range of 20.2-226.1 %. Thtal observed annual landings in 1993 and 1994 were 16, 61OM/T and 1O, 748M/T respectively, while the model predicted 10, 7 48M/T and 8, 203M/T(APE 37.0% and 23.7%, respectively).

• Korean Journal of Fisheries and Aquatic Sciences
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• v.29 no.2
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• pp.143-149
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• 1996

Forecasts of the monthly catches of anchovy in Korea were carried out by the seasonal Autoregressive Integrated Moving Average (ARIMA) model and spectral analysis. The seasonal ARIMA model is as follows: $$(1-0.431B)(1-B^{12})Z_t=(1-0.882B^{12})e_t$$ where: $Z_t=value$ at month $t;\;B^{p}$ is a backward shift operator, that is, $B^pZ_t=Z_{t-p};$ and $e_t=error$ term at month t, which is to forecast 24 months ahead the anchovy catches in Korea. The prediction error by the Box-Cox transformation on monthly anchovy catches in Korea was less than that by the logarithmic transformation. The equation of the Box-Cox transformation was $Y'=(Y^{0.58}-1)/0.58$. Forecasts of the monthly anchovy catches for $1991\~1992$, which were compared with the actual catches, had an absolute percentage error (APE) range of $1.0\~63.2\%$. Total observed annual catches in 1991 and 1992 were 170,293 M/T and 168,234 M/T respectively, while the predicted catches were 148,201 M/T and 148,834 M/T $(API\;13.0\%\;and\;11.5\%,\;respectively)$. The spectrum analysis of the monthly catches of anchovy showed some dominant fluctuations in the periods of 2.2, 6.1, 10.2 12.0 and 14.7 months. The spectrum analysis was also useful for selecting the ARIMA model.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.28 no.5
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• pp.589-598
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• 1995

Filtering rates of Crassostrea gigas were experimentally investigated with reference to effects of water temperature and size. Absorptiometric determinations of filtering rates with oysters being fed diatom Chaetoceros calcirtans were carried out in a closed system. Optical density of 675nm in path length 100mm cell used as the indication of food particles absorption was appeared directly In proportion with the concentration of diatom pigment $chlorophyll-\alpha$. In the closed system where $C_0$ is $OD_{675}$ at initial time 0, $C_t$, at time t, and Z is the decreasing coefficient of OD as meaning of instantaneous removal speed, then $C_t=C_0{\cdot} e^{-2t}$, $Z=In(C_t/C_0)/t$. On the assumption that the filtering rate is constant, then removal rate per unit time (d) is $d=-e^{-z}$. If t is used to time unit of hour (hr), the filtering rate (FR) in I/hr is given by $FR=V{\cdot}d=V(1-e^{-z})$, where V is the water volume (I) of the experimental vessel. Filtering rate increased as exponential function with increasing temperature while not over critical limit. The critical temperature for filtering rate was assumed to be between $28^{\circ}C$ and $29^{\circ}C$. And the weight exponent for filtering rate is 0.223. The model formula derived from the results as FR, $Ihr^{-1}$ = $Exp(0.208{\cdot}T-4.324){\cdot} (DW)^{0.223}$ (T<29 $^{\circ}C)$ where T is water temperature $(^{\circ}C

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.19 no.1
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• pp.69-81
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• 2015

The present paper deals with the determination of temperature, displacement and thermal stresses in a semi-infinite hollow circular disk due to internal heat generation within it. Initially the disk is kept at arbitrary temperature F(r, z). For times t > 0 heat is generated within the circular disk at a rate of g(r, z, t) $Btu/hr.ft^3$. The heat flux is applied on the inner circular boundary (r = a) and the outer circular boundary (r = b). Also, the lower surface (z = 0) is kept at temperature $Q_3(r,t)$ and the upper surface ($Z={\infty}$) is kept at zero temperature. Hollow circular disk extends in the z-direction from z = 0 to infinity. The governing heat conduction equation has been solved by using finite Hankel transform and the generalized finite Fourier transform. As a special case mathematical model is constructed for different metallic disk have been considered. The results are obtained in series form in terms of Bessel's functions. These have been computed numerically and illustrated graphically.

• Journal of Environmental Impact Assessment
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• v.22 no.6
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• pp.591-607
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• 2013

The objectives of the study were to evaluate the aquatic environment of an urban stream using various ecological parameters of biological biomarkers, physical habitat quality and chemical water quality and to develop a "Multimetric Eco-Model" ($M_m$-E Model) for the ecosystem evaluations. For the applications of the $M_m$-E model, three zones including the control zone ($C_Z$) of headwaters, transition zone ($T_Z$) of mid-stream and the impacted zone ($I_Z$) of downstream were designated and analyzed the seasonal variations of the model values. The biomarkers of DNA, based on the comet assay approach of single-cell gel electrophoresis (SCGE), were analyzed using the blood samples of Zacco platypus as a target species, and the parameters were used tail moment, tail DNA(%) and tail length (${\mu}m$) in the bioassay. The damages of DNA were evident in the impacted zone, but not in the control zone. The condition factor ($C_F$) as key indicators of the population evaluation indicator was analyzed along with the weight-length relation and individual abnormality. The four metrics of Qualitative Habitat Evaluation Index (QHEI) were added for the evaluations of physical habitat. In addition, the parameters of chemical water quality were used as eutrophic indicators of nitrogen (N) and phosphorus (P), chemical oxygen demand (COD) and conductivity. Overall, our results suggested that attributes of biomarkers and bioindicators in the impacted zone ($I_Z$) had sensitive response largely to the chemical stress (eutrophic indicators) and also partially to physical habitat quality, compared to the those in the control zone.

• Journal of Periodontal and Implant Science
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• v.48 no.3
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• pp.182-192
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• 2018

Purpose: The purpose of the present study was to validate an experimental model for assessing tissue integration of titanium and zirconia implants with and without buccal dehiscence defects. Methods: In 3 dogs, 5 implants were randomly placed on both sides of the mandibles: 1) Z1: a zirconia implant (modified surface) within the bony housing, 2) Z2: a zirconia implant (standard surface) within the bony housing, 3) T: a titanium implant within the bony housing, 4) Z1_D: a Z1 implant placed with a buccal bone dehiscence defect (3 mm), and 5) T_D: a titanium implant placed with a buccal bone dehiscence defect (3 mm). The healing times were 2 weeks (one side of the mandible) and 6 weeks (the opposite side). Results: The dimensions of the peri-implant soft tissue varied depending on the implant and the healing time. The level of the mucosal margin was located more apically at 6 weeks than at 2 weeks in all groups, except group T. The presence of a buccal dehiscence defect did not result in a decrease in the overall soft tissue dimensions between 2 and 6 weeks ($4.80{\pm}1.31$ and 4.3 mm in group Z1_D, and $4.47{\pm}1.06$ and $4.5{\pm}1.37mm$ in group T_D, respectively). The bone-to-implant contact (BIC) values were highest in group Z1 at both time points ($34.15%{\pm}21.23%$ at 2 weeks, $84.08%{\pm}1.33%$ at 6 weeks). The buccal dehiscence defects in groups Z1_D and T_D showed no further bone loss at 6 weeks compared to 2 weeks. Conclusions: The modified surface of Z1 demonstrated higher BIC values than the surface of Z2. There were minimal differences in the mucosal margin between 2 and 6 weeks in the presence of a dehiscence defect. The present model can serve as a useful tool for studying peri-implant dehiscence defects at the hard and soft tissue levels.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2001.11a
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• pp.112-119
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• 2001

Parameter tuning methods by Ziegler-Nickels for control systems are generally classified into Z-N(1) and Z-N(2). The purpose of this paper is to describe what relations exist between methods of Z-N(1) and Z-N(2), or how Z-N(1) method can be originated from Z-N(2) method by analyzing one loop control system of P or PI controller and time delay process. The formulas of Z-N(1) consist of process parameters, L(time delay), $K_m$(gain) and $T_m$(time constant), but Z-N(2) method is based only on the ultimate gain $K_u$ and the ultimate period $T_u$ acquired normally by practical trial without any parameters of Z-N(1). In this paper, for the first step to seek mutual relations, the simple formulas of Z-N(2) are transformed into the formulas composed of the same parameters as Z-N(1) which is derived from the analysis of frequency characteristics. Then, the approximation of the actual ultimate frequency is proposed as important premise in the translation between Z-N(1) and (2). Such equalization and approximation brings a simple approximated formula which can explain how Z-N(1) is originated from the Z-N(2) in the form of formula. And a model system is adopted to compare the approximated formula to Z-N(1) and Z-N(2) methods, the results of which show the effectiveness of the proposals.

• Biomedical Science Letters
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• v.18 no.3
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• pp.210-217
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• 2012

Oncolytic viral vectors have shown good candidates for cancer treatment but have many limitations. To improve the therapeutic potential of oncolytic vaccinia virus, we developed a recombinant vaccinia virus expressing the 4-1BBL co-stimulatory molecule or CCL21. 4-1BBL and CCL21 expression was identified by FACS analysis and immunoblotting. rV-4-1BBL vaccination shows significant tumor regression compared to rV-LacZ, but rV-CCL21 shows rapid tumor growth compared to rV-LacZ in the poorly immunogenic B16 murine melanoma model. 4-1BBL expression resulted in the increase of the number of CD8+ T cells and especially the increase of effector (CD62L-CD44+) CD8+ T cells. These data suggest 4-1BBL may be the potential target for enhancement of tumor immunotherapy.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1069-1072
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• 1987

In this paper, we drive useful data from 3-D depth information as input using discontinuity boundary or clustering. And using magnitude and direction of z-gradient we classify the data into adaptable primitive types through intrinsic and stochastical processing. After these processing information is reconstructed for forming data base. And make relationship and standard view position for matching.